skimage2.filters.gabor_kernel#

skimage2.filters.gabor_kernel(frequency, theta=0, bandwidth=1, sigma_x=None, sigma_y=None, n_stds=3, offset=0, dtype=<class 'numpy.complex128'>)[source]#

Return complex 2D Gabor filter kernel.

Gabor kernel is a Gaussian kernel modulated by a complex harmonic function. Harmonic function consists of an imaginary sine function and a real cosine function. Spatial frequency is inversely proportional to the wavelength of the harmonic and to the standard deviation of a Gaussian kernel. The bandwidth is also inversely proportional to the standard deviation.

Parameters:
frequencyfloat

Spatial frequency of the harmonic function. Specified in pixels.

thetafloat, optional

Orientation in radians. If 0, the harmonic is in the x-direction.

bandwidthfloat, optional

The bandwidth captured by the filter. For fixed bandwidth, sigma_x and sigma_y will decrease with increasing frequency. This value is ignored if sigma_x and sigma_y are set by the user.

sigma_x, sigma_yfloat, optional

Standard deviation in x- and y-directions. These directions apply to the kernel before rotation. If theta = pi/2, then the kernel is rotated 90 degrees so that sigma_x controls the vertical direction.

n_stdsscalar, optional

The linear size of the kernel is n_stds (3 by default) standard deviations

offsetfloat, optional

Phase offset of harmonic function in radians.

dtype{np.complex64, np.complex128}

Specifies if the filter is single or double precision complex.

Returns:
gcomplex array

Complex filter kernel.

References

Examples

>>> from _skimage2.filters import gabor_kernel
>>> from matplotlib import pyplot as plt

>>> gk = gabor_kernel(frequency=0.2)
>>> fig, ax = plt.subplots()
>>> ax.imshow(gk.real)
>>> plt.show()

>>> # more ripples (equivalent to increasing the size of the
>>> # Gaussian spread)
>>> gk = gabor_kernel(frequency=0.2, bandwidth=0.1)
>>> fig, ax = plt.suplots()
>>> ax.imshow(gk.real)
>>> plt.show()